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Global and Local Covert Visual Attention: Evidence From A Bayesian Hidden Markov Model

Published online by Cambridge University Press:  01 January 2025

John Liechty,
Rik Pieters and
Michel Wedel
John Liechty*
Affiliation:
Pennsylvania State University
Rik Pieters
Affiliation:
University of Tilburg
Michel Wedel
Affiliation:
University of Michigan
*
Requests for reprints should be sent to John Liechty, Department of Marketing, Smeal College of Business Administration, Pennsylvania State University, 701 Business Administration Building, University Park PA 16802-3007. E-Mail: jc112@psu.edu
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Abstract

Psychological, psychophysical and physiological research indicates that people switch between two covert attention states, local and global attention, while visually exploring complex scenes. The focus in the local attention state is on specific aspects and details of the scene, and on examining its content with greater visual detail. The focus in the global attention state is on exploring the informative and perceptually salient areas of the scene, and possibly on integrating the information contained therein. The existence of these two visual attention states, their relative prevalence and sequence in time has remained empirically untested to date. To fill this gap, we develop a psychometric model of visual covert attention that extends recent work on hidden Markov models, and we test it using eye-movement data. The model aims to describe the observed time series of saccades typically collected in eye-movement research by assuming a latent Markov process, indicative of the brain switching between global and local covert attention. We allow subjects to be in either state while exploring a stimulus visually, and to switch between them an arbitrary number of times. We relax the no-memory-property of the Markov chain. The model that we develop is estimated with MCMC methodology and calibrated on eye-movement data collected in a study of consumers' attention to print advertisements in magazines.

Keywords

change point modelsreversible jump Markov Chain Monte Carlovariable dimension time-series modelsvisual attentioncovert attentionovert attentioneye-movementssaccades

Information

Type
2003 Presidential Address
Information
Psychometrika , Volume 68 , Issue 4 , December 2003, pp. 519 - 541
Copyright
Copyright © 2003 The Psychometric Society

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Footnotes

We thank Dominique Claessens of Verify International for making the data available to us.

References

Anstis, S.M. (1974). A chart demonstrating variation in acuity with retinal position. Vision Research, 14, 589–592.CrossRefGoogle ScholarPubMed
Antes, J.R. (1974). The time course of picture viewing. Journal of Experimental Psychology, 103, 62–70.10.1037/h0036799CrossRefGoogle ScholarPubMed
Egeth, H.E., Yantis, S. (1997). Visual attention: Control, representation, and time course. Annual Review of Psychology, 48, 269–297.10.1146/annurev.psych.48.1.269CrossRefGoogle ScholarPubMed
Ellis, S.R., Smith, J.D. (1985). Patterns of statistical dependency in visual scanning. In Groner, R., McConkie, G. W., Menz, C. (Eds.), Eye movements and human information processing (pp. 221–238). Amsterdam: Elsevier Science Publishers.Google Scholar
Engbert, R., Kliegl, R. (2001). Mathematical models of eye-movements in reading: A possible role for autonomous saccades. Biological Cybernetics, 85, 77–87.10.1007/PL00008001CrossRefGoogle Scholar
Findlay, J.M., Gilchrist, I.D. (1998). Eye guidance and visual search. In Underwood, G. (Eds.), Eye guidance in reading and scene perception (pp. 295–312). Amsterdam: Elsevier.10.1016/B978-008043361-5/50014-6CrossRefGoogle Scholar
Gelman, A., Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–511.CrossRefGoogle Scholar
Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (1996). Markov Chain Monte Carlo in practice. Boca Raton, FL: Chapman and Hall/CRC.Google Scholar
Green, P.J. (1995). Reversible jump MCMC computation and Bayesian model determination. Biometrika, 82, 711–732.CrossRefGoogle Scholar
Groner, R. (1988). Eye movements, attention and visual information processing: Some empirical results and methodological considerations. In Lüer, G., Lass, U., Shallo-Hoffman, J. (Eds.), Eye movement research: Physiological and psychological aspects (pp. 295–319). Toronto, Canada: Hogrefe.Google Scholar
Hacisalihzade, S.S., Stark, L.W., Allen, J.S. (1992). Visual perception and sequences of eye movement fixations: A stochastic modeling approach. IEEE Transactions on Systems, Man, and Cybernetics, 22(3), 474–481.CrossRefGoogle Scholar
Henderson, J.M., Hollingworth, A. (1998). Eye movements during scene viewing: An overview. In Underwood, G. (Eds.), Eye guidance in reading and scene perception (pp. 269–293). Amsterdam: Elsevier.10.1016/B978-008043361-5/50013-4CrossRefGoogle Scholar
Henderson, J.M., Hollingworth, A. (1999). High-level scene perception. Annual Review of Psychology, 50, 243–271.10.1146/annurev.psych.50.1.243CrossRefGoogle ScholarPubMed
Hodgson, M.E.A., Green, P.J. (1999). Investigating Markov model discrimination for ion channels. Journal of the Royal Statistical Society, 455, 3425–3448.Google Scholar
Inhoff, A.W., Radach, R. (1998). Definition and computation of oculomotor measures in the study of cognitive processes. In Underwood, G. (Eds.), Eye guidance in reading and scene perception (pp. 29–53). Amsterdam: Elsevier.10.1016/B978-008043361-5/50003-1CrossRefGoogle Scholar
Itti, L., Koch, C. (2001). Computational modelling of visual attention. Nature Neuroscience, 2, 1–11.Google ScholarPubMed
Itti, L., Koch, C., Niebur, E. (1998). A model of saliency based visual attention for rapid scene analysis. IEEE Transactions of Pattern and Analytical Machine Intelligence, 20, 1254–1259.10.1109/34.730558CrossRefGoogle Scholar
Just, A.M., Carpenter, P.A. (1987). The psychology of reading and language comprehension. Boston, MA: Allyn and Bacon.Google Scholar
Klein, R. (1988). Inhibitory tagging system facilitates visual search. Nature, 334, 430–431.10.1038/334430a0CrossRefGoogle ScholarPubMed
LaBerge, D. (1998). Attentional emphasis in visual orienting and resolving. In Wright, R.D. (Eds.), Visual attention (pp. 417–454). New York, NY: Oxford University Press.10.1093/oso/9780195126938.003.0016CrossRefGoogle Scholar
Lévy-Schoen, A. (1981). Flexible and/or rigid control of oculomotor scanning behavior. In Fischer, D.F., Mony, R.A., Senders, J.W. (Eds.), Eye-movements: Cognition and visual perception (pp. 299–314). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Liechty, J.C., Roberts, G.O. (2001). Markov Chain Monte Carlo methods for switching diffusion models. Biometrika, 88(2), 299–315.CrossRefGoogle Scholar
McConkie, G.W. (1983). Eye movements and perception during reading. In Rayner, K. (Eds.), Eye movements in reading: Perceptual and language processes (pp. 65–96). New York, NY: Academic Press.CrossRefGoogle Scholar
McConkie, G.W., Reddix, M.R., Zola, D. (1992). Perception and cognition in reading: Where is the meeting point. In Rayner, K. (Eds.), Eye movements and vision cognition: Scene perception and reading (pp. 293–303). New York, NY: Springer Verlag.10.1007/978-1-4612-2852-3_17CrossRefGoogle Scholar
Monk, T.H. (1984). Search. In Warm, J.S. (Eds.), Sustained attention in human performance (pp. 293–321). New York, NY: John Wiley & Sons.Google Scholar
Newton, M.A., Raftery, A.E. (1994). Approximate Bayesian inference by the weighted likelihood bootstrap (with Discussion). Journal of the Royal Statistical Society, 56, 3–28.10.1111/j.2517-6161.1994.tb01956.xCrossRefGoogle Scholar
Ober, J. (1994). Infra-red reflection techniques. In Ygge, J., Lennerstrand, G. (Eds.), Eye movements in reading (pp. 9–19). Tarrytown, NJ: Elsevier Science.Google Scholar
O'Hagan, A. (1994). Kendall's advanced theory of statistics: Volume 2B Bayesian inference. New York, NY: John Wiley & Sons.Google Scholar
Palmer, S.E. (1999). Vision science: Photons to phenomenology. Cambridge, MA: The MIT Press.Google Scholar
Pieters, R., Rosbergen, E., Wedel, M. (1999). Visual attention to repeated print Advertising: A test of scanpath theory. Journal of Marketing Research, 36, 424–438.CrossRefGoogle Scholar
Pieters, R., Warlop, L., Wedel, M. (2002). Breaking through the clutter: Benefits of advertisement originality and familiarity for brand attention and memory. Management Science, 48(6), 765–781.10.1287/mnsc.48.6.765.192CrossRefGoogle Scholar
Pomplun, M. (1998). Analysis and models of eye movements in comparative visual search. Gottingen, Germany: Cuvillier.Google Scholar
Posner, M.I. (1980). Orienting of attention. Quarterly Journal of Experimental Psychology, 32, 3–25.10.1080/00335558008248231CrossRefGoogle ScholarPubMed
Posner, M.I., Cohen, Y. (1984). Components of visual orienting. In Bouma, H., Bouwhuis, D. (Eds.), Attention and performance X (pp. 531–556). Hove, U.K.: Lawrence Erlbaum Associate.Google Scholar
Rayner, K. (1998). Eye movement in reading and information processing: 20 Years of research. Psychological Bulletin, 124, 372–422.10.1037/0033-2909.124.3.372CrossRefGoogle ScholarPubMed
Rayner, K., Rotello, C.M., Stewart, A.J., Keir, J., Duffy, S.A. (2001). Integrating text and pictorial information: Eye movements when looking at print advertisements. Journal of Experimental Psychology: Applied, 7(1), 219–226.Google ScholarPubMed
Reichle, E.D., Pollatsek, A., Fischer, D.L., Rayner, K. (1998). Toward a model of eye movement control in reading. Psychological Review, 105(1), 125–157.10.1037/0033-295X.105.1.125CrossRefGoogle Scholar
Rimey, R.D., Brown, C.M. (1991). Controlling eye movements with hidden Markov models. International Journal of Computer Vision, 7(1), 47–65.CrossRefGoogle Scholar
Robert, C.P., Ryden, T., Titterington, D.M. (2000). Bayesian inference in hidden Markov models through the reversible jump Markov Chain Monte Carlo Method. Journal of the Royal Statistical Society, 62, 57–76.10.1111/1467-9868.00219CrossRefGoogle Scholar
Rosbergen, E., Pieters, R., Wedel, M. (1997). Visual attention to advertising: A segment-level analysis. Journal of Consumer Research, 24, 305–314.10.1086/209512CrossRefGoogle Scholar
Ross, S.M. (1983). Stochastic processes. New York, NY: John Wiley & Sons.Google Scholar
Salvucci, D.D., Anderson, J.R. (2001). Automated eye movement protocol analysis. Human Computer Interaction, 16, 39–86.10.1207/S15327051HCI1601_2CrossRefGoogle Scholar
Sperling, G., Weichselgartner, E. (1995). Episodic theory of the dyanmics of spatial attention. Psychological Review, 102(3), 503–532.10.1037/0033-295X.102.3.503CrossRefGoogle Scholar
Stolk, H., Boon, K., Smulders, M. (1993). Visual information processing in a study task using text and pictures. In d'Ydewalle, G., van Rensbergen, J. (Eds.), Perception and cognition: Advances in eye movement research (pp. 285–296). Amsterdam: North-Holland.Google Scholar
Underwood, G., Radach, R. (1998). Eye guidance and human information processing: Reading, visual search, picture perception, and driving. In Underwood, G. (Eds.), Eye guidance in reading and scene perception (pp. 1–28). Oxford, England: Elsevier.Google Scholar
Vecera, S.P., Farah, M.J. (1994). Does visual attention select objects or locations?. Journal of Experimental Psychology: General, 123(2), 146–160.10.1037/0096-3445.123.2.146CrossRefGoogle ScholarPubMed
Viviani, P. (1990). Eye movements in visual search: Cognitive, perceptual and motor control aspects. In Kowler, E. (Eds.), Eye movements and their role in visual and cognitive processes (pp. 353–393). Amsterdam: Elsevier Science Publishers.Google Scholar
Wedel, M., Pieters, R. (2000). Eye fixations on advertisements and memory for brands: A model and findings. Marketing Science, 19, 297–312.10.1287/mksc.19.4.297.11794CrossRefGoogle Scholar
Wolfe, J.M. (1998). Visual search. In Pashler, H. (Eds.), Attention (pp. 13–71). Hove, U.K.: Psychology Press.Google Scholar
Wright, R.D., Ward, L.M. (1998). The control of attention. In Wright, R.D. (Eds.), Visual attention (pp. 132–186). New York, NY: Oxford University Press.10.1093/oso/9780195126938.003.0006CrossRefGoogle Scholar
Yarbus, A.L. (1967). Eye movements and vision. New York, NY: Plenum Press.10.1007/978-1-4899-5379-7CrossRefGoogle Scholar
Zangemeister, W.H., Sherman, K., Stark, L. (1995). Evidence for a global scanpath strategy in viewing abstract compared with realistic pictures. Neuropsychologia, 33, 1009–1025.10.1016/0028-3932(95)00014-TCrossRefGoogle Scholar